Currently in the 300 mm semiconductor fabricator, there is no relationship between the fab-wide Advanced Process Control (APC) system and the Lot Sampling Engine (LSE). As a separate system, the APC system relies on pre-measurement, post-measurement and operational data to calculate the recipe parameter required for the process tool to perform the wafer process on target. Ideal for optimizing the recipe parameter adjustment (RPA) is to measure every wafer. However, there is a tradeoff with the cost of producing the wafers and wasting cycle time.
Before utilization of the LSE, sampling was fixed by route, e.g., determined by a lot attribute assigned at a beginning or early stage of a manufacturing process, and not linked to manufacturing process capability and/or performance. The attribute assured that a certain percentage of work-in-progress (WIP) was measured at various inspection points before and/or after each operation of a production process. Unfortunately, the attribute did not account for performance of the operation. Thus, when the process was performing poorly, not enough lots were being measured, and when the process was performing well, too many lots were being measured, thus wasting cycle time.
Thus, the LSE was developed, which provides a sampling plan to optimize the throughput of the process. From manufacturing's perspective, measurement is an overhead and it has no value if it is not really necessary to be measured. When the process is performing well, the manufacturer may be measuring too many lots and wasting cycle time. However, with the LSE, the sampling rate is linked to the process capability. With this smart sampling method, the cycle time is no longer wasted on processes that are performing well. Instead, the focus is on those processes that need more measurements for process improvement.
However, there is a problem when the LSE decides to bypass the measurement (e.g., for throughput benefit), in that the APC system is not able to calculate the optimized value for the recipe parameter adjustment (RPA) due to limited number of measurements (yield degradation). The LSE uses a process capability index (Cp/Cpk metrics) to adjust sampling rates and reduce the Mean Time To Detect (MTTD). Determining process capability involves measuring a variability of a process and comparing the measured variability with a proposed specification or product tolerance. However, the Cp/Cpk metrics are calculated over a 28 day period and are only updated once a week. So, the LSE does not react to the tool/process issues quickly enough.
Another challenge is created by “Send Ahead” (SAHD) operations. In normal manufacturing operations, there is no need to use, for example, SAHD wafers. However, when tolerance variation in a particular process is unacceptable, SAHD is required in order to prevent scrap. SAHD is also necessary for low volume parts to speed yield learning. In either case, no SAHD lot should be skipped by a sampling plan. However, when relying on an attribute to determine sampling, many SAHD lots are not sampled. Without real time integrated product and process information, it is difficult, if not impossible, to establish a sampling rate that can account for a lot attribute, process performance, and SAHD lots in a manufacturing process. When relying solely on a lot attribute, or manual sampling, a lot may be sampled too often, or not often enough. Sampling, i.e., measurement, is a non-value added operation and actually slows production. Thus, over sampling can be costly. However, if too few lots are sampled, defective lots can pass through production. In this case, final testing costs are increased and a company's reputation for quality may be at risk.
However, there is no known system that correlates the benefits of both the APC system and the LSE system, by providing real-time LSE information to the APC system, such that both systems are integrated and optimized for the lot sampling plan without affecting the APC operations.
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.